Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2001

Abstract

This paper introduces a class of predictive self-organizing neural networks known as Adaptive Resonance Associative Map (ARAM) for classification of free-text documents. Whereas most sta- tistical approaches to text categorization derive classification knowledge based on training examples alone, ARAM performs supervised learn- ing and integrates user-defined classification knowledge in the form of IF-THEN rules. Through our experiments on the Reuters-21578 news database, we showed that ARAM performed reasonably well in mining categorization knowledge from sparse and high dimensional document feature space. In addition, ARAM predictive accuracy and learning efficiency can be improved by incorporating a set of rules derived from the Reuters category description. The impact of rule insertion is most significant for categories with a small number of relevant documents.

Keywords

Data mining, Information retrieval systems, Knowledge based systems, Neural networks

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | OS and Networks

Research Areas

Data Science and Engineering

Publication

Advances in knowledge discovery and data mining 5th Pacific Asia conference: Proceedings, 5th Pacific-Asia Conference: Hong Kong, April 16-18

Volume

2035

First Page

66

Last Page

77

ISBN

9783540419105

Identifier

10.1007/3-540-45357-1_10

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/3-540-45357-1_10

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